Modern mechanical systems can be exceedingly complex. The complexities of modern mechanical systems have led to increasing needs for automated prognosis and fault detection systems. These prognosis and fault detection systems are designed to monitor the mechanical system in an effort to predict the future performance of the system and detect potential faults. These systems are designed to detect these potential faults such that the potential faults can be addressed before the potential faults lead to failure in the mechanical system.
One type of mechanical system where prognosis and fault detection is of particular importance is aircraft systems. In aircraft systems, prognosis and fault detection can detect potential faults such that they can be addressed before they result in serious system failure and possible in-flight shutdowns, take-off aborts, delays or cancellations.
Modern aircraft are increasingly complex. The complexities of these aircraft have led to an increasing need for automated fault detection systems. These fault detection systems are designed to monitor the various systems of the aircraft in an effort to detect potential faults. These systems are designed to detect these potential faults such that the potential faults can be addressed before the potential faults lead to serious system failure and possible in-flight shutdowns, take-off aborts, delays or cancellations.
Turbine engines are a particularly critical part of many aircraft. Turbine engines are commonly used for main propulsion aircraft. Furthermore, turbine engines are commonly used in auxiliary power units (APUs) that are used to generate auxiliary power and compressed air for use in the aircraft. Given the critical nature of turbine engines in aircraft, the need for fault detection in turbine engines is of extreme importance.
Traditional fault detection systems for turbine engines have been limited in their ability to detect the occurrence of erosion in turbine blades. Erosion in compressor blades can result in serious blade damage, which can cause severe performance problems in the turbine engines. Unfortunately, previous fault detection methods have been unable to suitably detected erosion in the compressor blades with sufficient accuracy based on the limited data sets available for fault detection. Other fault detection methods have relied upon using devices such as borescopes for visual inspection of the turbine blades. These methods are also limited, as they typically require removal of the engine, thus resulting in excessive costs and vehicle downtime.
Thus, what is needed is an improved system and method for detecting erosion in turbine blades that can consistently detect erosion from engine faults from limited and sometimes noisy engine data sets.